ŠEBESTOVÁ, Eva, Jaroslav BENDL, Jan BREZOVSKÝ and Jiří DAMBORSKÝ. Computational Tools for Designing Smart Libraries. In Gillam, E. M. J., Copp, J. N., Ackerley, D. F.,. Directed Evolution Library Creation. New York: Springer New York, 2014, p. 291-314. 2nd edition, 1197. ISBN 978-1-4939-1052-6. Available from: https://dx.doi.org/10.1007/978-1-4939-1053-3_20.
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Basic information
Original name Computational Tools for Designing Smart Libraries
Authors ŠEBESTOVÁ, Eva (203 Czech Republic, belonging to the institution), Jaroslav BENDL (203 Czech Republic, belonging to the institution), Jan BREZOVSKÝ (203 Czech Republic, belonging to the institution) and Jiří DAMBORSKÝ (203 Czech Republic, guarantor, belonging to the institution).
Edition New York, Directed Evolution Library Creation, p. 291-314, 24 pp. 2nd edition, 1197, 2014.
Publisher Springer New York
Other information
Original language English
Type of outcome Chapter(s) of a specialized book
Field of Study 10600 1.6 Biological sciences
Country of publisher United States of America
Confidentiality degree is not subject to a state or trade secret
Publication form printed version "print"
WWW URL
RIV identification code RIV/00216224:14310/14:00093102
Organization unit Faculty of Science
ISBN 978-1-4939-1052-6
Doi http://dx.doi.org/10.1007/978-1-4939-1053-3_20
UT WoS 000343250100021
Keywords in English Computational tool; Rational design; Smart library; Focused library; Directed evolution; Mutation; Protein engineering; Software Web tool
Tags AKR
Tags International impact, Reviewed
Changed by Changed by: Mgr. Marie Šípková, DiS., učo 437722. Changed: 23/6/2020 15:00.
Abstract
Traditional directed evolution experiments are often time-, labor- and cost-intensive because they involve repeated rounds of random mutagenesis and the selection or screening of large mutant libraries. The efficiency of directed evolution experiments can be significantly improved by targeting mutagenesis to a limited number of hot-spot positions and/or selecting a limited set of substitutions. The design of such “smart” libraries can be greatly facilitated by in silico analyses and predictions. Here we provide an overview of computational tools applicable for (a) the identification of hot-spots for engineering enzyme properties, and (b) the evaluation of predicted hot-spots and selection of suitable amino acids for substitutions. The selected tools do not require any specific expertise and can easily be implemented by the wider scientific community.
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